1 code implementation • 17 Dec 2009 • Patrick L. Combettes, Jean-Christophe Pesquet
The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set.
Optimization and Control Numerical Analysis 90C25, 65K05, 90C90, 94A08
no code implementations • 30 Jun 2011 • Patrick L. Combettes, Jean-Christophe Pesquet
We propose a primal-dual splitting algorithm for solving monotone inclusions involving a mixture of sums, linear compositions, and parallel sums of set-valued and Lipschitzian operators.
Optimization and Control 47H05, 90C25
no code implementations • 23 Dec 2011 • Lotfi Chaari, Sébastien Mériaux, Jean-Christophe Pesquet, Philippe Ciuciu
To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated.
no code implementations • 21 Mar 2014 • Giovanni Chierchia, Nelly Pustelnik, Beatrice Pesquet-Popescu, Jean-Christophe Pesquet
In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image.
no code implementations • 20 Jun 2014 • Nikos Komodakis, Jean-Christophe Pesquet
Optimization methods are at the core of many problems in signal/image processing, computer vision, and machine learning.
1 code implementation • 24 Jun 2014 • Jean-Christophe Pesquet, Audrey Repetti
Based on a preconditioned version of the randomized block-coordinate forward-backward algorithm recently proposed in [Combettes, Pesquet, 2014], several variants of block-coordinate primal-dual algorithms are designed in order to solve a wide array of monotone inclusion problems.
Optimization and Control 47H05, 49M29, 49M27, 65K10, 90C25
no code implementations • 15 Jan 2015 • G. Chierchia, Nelly Pustelnik, Jean-Christophe Pesquet, B. Pesquet-Popescu
In this paper, we propose a convex optimization approach for efficiently and exactly solving the multiclass SVM learning problem involving a sparse regularization and the multiclass hinge loss formulated by Crammer and Singer.
no code implementations • 24 Oct 2016 • Yosra Marnissi, Yuling Zheng, Emilie Chouzenoux, Jean-Christophe Pesquet
We demonstrate the potential of the proposed approach through comparisons with state-of-the-art techniques that are specifically tailored to signal recovery in the presence of mixed Poisson-Gaussian noise.
no code implementations • 27 Feb 2017 • Caroline Chaux, Laurent Duval, Jean-Christophe Pesquet
We propose a 2D generalization to the $M$-band case of the dual-tree decomposition structure (initially proposed by N. Kingsbury and further investigated by I. Selesnick) based on a Hilbert pair of wavelets.
no code implementations • 14 Jul 2017 • Anna Jezierska, Hugues Talbot, Jean-Christophe Pesquet, Gilbert Engler
Point spread function (PSF) plays an essential role in image reconstruction.
no code implementations • 18 Sep 2017 • Qi Wei, Emilie Chouzenoux, Jean-Yves Tourneret, Jean-Christophe Pesquet
This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior.
no code implementations • 25 Dec 2017 • Luis M. Briceno-Arias, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet
In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method.
no code implementations • 23 May 2018 • Viacheslav Dudar, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet, Vladimir Semenov
In this paper, we develop a novel second-order method for training feed-forward neural nets.
no code implementations • 22 Aug 2018 • Patrick L. Combettes, Jean-Christophe Pesquet
Motivated by structures that appear in deep neural networks, we investigate nonlinear composite models alternating proximity and affine operators defined on different spaces.
Optimization and Control
1 code implementation • 11 Dec 2018 • Carla Bertocchi, Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, Marco Prato
Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution.
no code implementations • 3 Mar 2019 • Patrick L. Combettes, Jean-Christophe Pesquet
Deriving sharp Lipschitz constants for feed-forward neural networks is essential to assess their robustness in the face of adversarial inputs.
Optimization and Control
no code implementations • 26 Apr 2019 • Emilie Chouzenoux, Henri Gérard, Jean-Christophe Pesquet
A wide array of machine learning problems are formulated as the minimization of the expectation of a convex loss function on some parameter space.
no code implementations • 18 Feb 2020 • Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet, Hamid Krim
On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest.
no code implementations • 5 Aug 2020 • Patrick L. Combettes, Jean-Christophe Pesquet
The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solve a great variety of problems.
Optimization and Control
no code implementations • 8 Oct 2020 • Sagar Verma, Nicolas Henwood, Marc Castella, Francois Malrait, Jean-Christophe Pesquet
In this paper, we explore the feasibility of modeling the dynamics of an electrical motor by following a data-driven approach, which uses only its inputs and outputs and does not make any assumption on its internal behaviour.
no code implementations • 29 Oct 2020 • Arthur Marmin, Marc Castella, Jean-Christophe Pesquet, Laurent Duval
We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate.
2 code implementations • 24 Dec 2020 • Jean-Christophe Pesquet, Audrey Repetti, Matthieu Terris, Yves Wiaux
Recently, several works have proposed to replace the operator related to the regularization by a more sophisticated denoiser.
Automated Theorem Proving Image Restoration Optimization and Control Image and Video Processing 47H05, 90C25, 90C59, 65K10, 49M27, 68T07, 68U10, 94A08
no code implementations • 1 Jan 2021 • Sagar Verma, Jean-Christophe Pesquet
Sparsifying deep neural networks is of paramount interest in many areas, especially when those networks have to be implemented on low-memory devices.
no code implementations • 21 Apr 2021 • Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet, Hamid Krim
Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.
1 code implementation • 14 Oct 2021 • Yunshi Huang, Emilie Chouzenoux, Jean-Christophe Pesquet
In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution.
1 code implementation • 14 Jun 2022 • Jérôme Rony, Jean-Christophe Pesquet, Ismail Ben Ayed
Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation.
no code implementations • 3 Sep 2022 • Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, Gabriele Scrivanti
The joint problem of reconstruction / feature extraction is a challenging task in image processing.
no code implementations • 27 Sep 2022 • Marion Savanier, Emilie Chouzenoux, Jean-Christophe Pesquet, Cyril Riddell
We unfold the Dual Block coordinate Forward-Backward (DBFB) algorithm, embedded in an iterative reweighted scheme, allowing the learning of key parameters in a supervised manner.
1 code implementation • 3 Oct 2022 • Yunshi Huang, Emilie Chouzenoux, Victor Elvira, Jean-Christophe Pesquet
Bayesian neural networks (BNNs) have received an increased interest in the last years.
1 code implementation • 26 Oct 2022 • Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed
We relax these assumptions and extend current benchmarks, so that the query-set classes of a given task are unknown, but just belong to a much larger set of possible classes.
1 code implementation • CVPR 2023 • Jérôme Rony, Jean-Christophe Pesquet, Ismail Ben Ayed
Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation.
1 code implementation • 20 Jun 2023 • Carlos Santos Garcia, Mathilde Larchevêque, Solal O'Sullivan, Martin Van Waerebeke, Robert R. Thomson, Audrey Repetti, Jean-Christophe Pesquet
A proximal algorithm based on a sparsity prior, dubbed SARA-COIL, has been further proposed to solve the associated inverse problem, to enable image reconstructions for high resolution COIL microendoscopy.
no code implementations • 31 Aug 2023 • Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini, Salla Latva-Aijo, Dominik Narnhofer, Jean-Christophe Pesquet, Sebastian J. Scott, Mahsa Yousefi
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework.
no code implementations • 9 Oct 2023 • Mathieu Vu, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed
Ensemble learning leverages multiple models (i. e., weak learners) on a common machine learning task to enhance prediction performance.
no code implementations • 29 Nov 2023 • Aymen Sadraoui, Ségolène Martin, Eliott Barbot, Astrid Laurent-Bellue, Jean-Christophe Pesquet, Catherine Guettier, Ismail Ben Ayed
This paper presents a new approach for classifying 2D histopathology patches using few-shot learning.
no code implementations • 30 Nov 2023 • Julien Ajdenbaum, Emilie Chouzenoux, Claire Lefort, Ségolène Martin, Jean-Christophe Pesquet
This makes the restoration of MPM images challenging.
no code implementations • 12 Dec 2023 • Nora Ouzir, Frédéric Pascal, Jean-Christophe Pesquet
In robust estimation, imposing classical constraints on the precision matrix, such as sparsity, has been limited by the non-convexity of the resulting cost function.
no code implementations • 30 Mar 2024 • Younes Belkouchi, Jean-Christophe Pesquet, Audrey Repetti, Hugues Talbot
This article introduces a novel approach to learning monotone neural networks through a newly defined penalization loss.
no code implementations • 7 Apr 2024 • Aurélie Beaufrère, Nora Ouzir, Paul Emile Zafar, Astrid Laurent-Bellue, Miguel Albuquerque, Gwladys Lubuela, Jules Grégory, Catherine Guettier, Kévin Mondet, Jean-Christophe Pesquet, Valérie Paradis
The diagnostic agreement between the pathological diagnosis and the model predictions in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively.